Mr. Hou Guan Ng Profile

Hou Guan Ng

at Univ Sains Malaysia

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Proceedings Article | 27 April 2009 Paper

Sea surface temperature algorithm with transmittance and sea surface emissivity dependence

H. Ng, M. MatJafri, K. Abdullah, C. Wong

Proceedings Volume 7334, 73341H (2009) https://doi.org/10.1117/12.820302

KEYWORDS: Transmittance, Sensors, Algorithm development, Temperature metrology, MODIS, Atmospheric sensing, Clouds, Infrared radiation, Data modeling, Black bodies

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The current sea surface temperature (SST) algorithms were derived empirically using a large amount of in-situ observation data. The algorithms derived had no guarantee to be used for the different regions and time. Large amount of in-situ data was required for the algorithm regression analysis. The new algorithm did not require a large amount of in-situ data. The algorithm requires additional input of transmittance and emissivity values. The transmittance depends on atmospheric profiles. The standard profile was used. The input sensor zenith angle and water vapor contents were changed within a certain range. The data were then simulated by MODTRAN to obtain the transmittance. The derived equation of sea surface emissivity as a function of sensor zenith was used. Brightness temperature, sea surface emissivity and transmittance values were use to calculate the sea surface temperature of each cloud free water pixel using an image processing software. The results show that the new algorithm produce a comparable the R²=0.6569 and RMSE= 1.24 K. The new algorithm did not require the large amount of in-situ SST data, but still can give the SST data in moderately high accuracy.

Proceedings Article | 7 October 2008 Paper

Merging of multi-temporal SST data at South China Sea

H. G. Ng, M. Z. MatJafri, K. Abdullah, H. S. Lim

Proceedings Volume 7114, 71140V (2008) https://doi.org/10.1117/12.800575

KEYWORDS: Clouds, Composites, Reflectivity, Infrared radiation, Infrared imaging, Calibration, Sensors, Remote sensing, Satellites, Radiometry

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The sea surface temperature (SST) mapping could be performed with a wide spatial and temporal extent in a reasonable time limit. The space-borne sensor of AVHRR was widely used for the purpose. However, the current SST retrieval techniques for infrared channels were limited only for the cloud-free area, because the electromagnetic waves in the infrared wavelengths could not penetrate the cloud. Therefore, the SST availability was low for the single image. To overcome this problem, we studied to produce the composite of three day's SST map. The diurnal changes of SST data are quite stable through a short period of time if no abrupt natural disaster occurrence. Therefore, the SST data of three consecutive days with nearly coincident daily time were merged in order to create a three day's composite SST data. The composite image could increase the SST availability. In this study, we acquired the level 1b AVHRR (Advanced Very High Resolution Radiometer) images from Malaysia Center of Remote Sensing (MACRES). The images were first preprocessed and the cloud and land areas were masked. We made some modifications on the technique of obtaining the threshold value for cloud masking. The SST was estimated by using the day split MCSST algorithm. The cloud free water pixels availability were computed and compared. The mean of SST for three day's composite data were calculated and a SST map was generated. The cloud free water pixels availability were computed and compared. The SST data availability was increased by merging the SST data.

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